Exploration of resting‐state brain functional connectivity as preclinical markers for arousal prediction in prolonged disorders of consciousness: A pilot study based on functional near‐infrared spectroscopy

Abstract Background There is no diagnostic assessment procedure with moderate or strong evidence of use, and evidence for current means of treating prolonged disorders of consciousness (pDOC) is sparse. This may be related to the fact that the mechanisms of pDOC have not been studied deeply enough and are not clear enough. Therefore, the aim of this study was to explore the mechanism of pDOC using functional near‐infrared spectroscopy (fNIRS) to provide a basis for the treatment of pDOC, as well as to explore preclinical markers for determining the arousal of pDOC patients. Methods Five minutes resting‐state data were collected from 10 pDOC patients and 13healthy adults using fNIRS. Based on the concentrations of oxyhemoglobin (HbO) and deoxyhemoglobin (HbR) in the time series, the resting‐state cortical brain functional connectivity strengths of the two groups were calculated, and the functional connectivity strengths of homologous and heterologous brain networks were compared at the sensorimotor network (SEN), dorsal attention network (DAN), ventral attention network (VAN), default mode network (DMN), frontoparietal network (FPN), and visual network (VIS) levels. Univariate binary logistic regression analyses were performed on brain networks with statistically significant differences to identify brain networks associated with arousal in pDOC patients. The receiver operating characteristic (ROC) curves were further analyzed to determine the cut‐off value of the relevant brain networks to provide clinical biomarkers for the prediction of arousal in pDOC patients. Results The results showed that the functional connectivity strengths of oxyhemoglobin (HbO)‐based SEN∼SEN, VIS∼VIS, DAN∼DAN, DMN∼DMN, SEN∼VIS, SEN∼FPN, SEN∼DAN, SEN∼DMN, VIS∼FPN, VIS∼DAN, VIS∼DMN, HbR‐based SEN∼SEN, and SEN∼DAN were significantly reduced in the pDOC group and were factors that could reflect the participants' state of consciousness. The cut‐off value of resting‐state functional connectivity strength calculated by ROC curve analysis can be used as a potential preclinical marker for predicting the arousal state of subjects. Conclusion Resting‐state functional connectivity strength of cortical networks is significantly reduced in pDOC patients. The cut‐off values of resting‐state functional connectivity strength are potential preclinical markers for predicting arousal in pDOC patients.


INTRODUCTION
Disorders of consciousness (DOC) are the most common complication of severe brain injury, with prolonged disorders of consciousness (pDOC) defined as DOC for more than 28 days (Song et al., 2020), including the unresponsive wakefulness syndrome (UWS) and the minimally conscious state (MCS) (Matsumoto-Miyazaki et al., 2016).
Relevant epidemiological studies have reported that ∼10%-15% of patients develop pDOC after brain injury (Andriessen et al., 2011;Y. Li et al., 2015).They may die or regain consciousness or may remain in the state of unconscious or minimally conscious for longer.Its uncertain efficacy, difficult to judge prognosis, and heavy economic burden pose many social and economic problems for clinical decision-making (Xie, Wang, Jia et al., 2022;Xie, Wang, Xie et al., 2022).However, there is no diagnostic assessment procedure with moderate or strong evidence of use (Estraneo et al., 2017).Standardized behavior assessment still is the "gold standard" for detecting signs of consciousness (Morrissey et al., 2018).But even the most accepted and recommended assessment of Coma Recovery Scale-Revised (CRS-R) may lead to the misdiagnosis rate of pDOC patients reaching 36% (Wannez et al., 2017a).It is possible that a positive electromyogram response to command, electroencephalogram (EEG) reactivity to sensory stimuli, laser-evoked potentials, and the Perturbational Complexity Index can distinguish MCS from UWS (Giacino et al., 2018), but none of them provide preclinical markers for predicting arousal in pDOC patients.
In addition to this, the evidence for current means of treating pDOC is sparse, and there is no consistent treatment guideline; only several drug (Giacino, Whyte, et al., 2012;Jha et al., 2008;Krimchansky et al., 2004;Passler & Riggs, 2001;Reynolds et al., 2013;Tucker & Sandhu, 2016) and non-drug treatments (Demirtas-Tatlidede et al., 2012;Giacino, Fins, et al., 2012;Louise-Bender et al., 2009;Popernack et al., 2015) are available.This may be related to the fact that the mechanisms of pDOC have not been studied deeply enough and are not clear enough.Therefore, the aim of this study was to explore the mechanism of pDOC using functional near-infrared spectroscopy (fNIRS) to provide a basis for the treatment of pDOC, as well as to explore preclinical markers for predicting the arousal of pDOC patients.
In recent years, advanced neuroimaging techniques have strongly contributed to capture these changes in functional connections associated with pDOC and promoted a rapid progress in the deeper understanding of the disease.Using advanced neuroimaging technology to directly detect the brain functional network of pDOC patients by bypassing behavioral response can reveal some hidden consciousness and cognition in patients' brains earlier (Fernandez-Espejo et al., 2015;Owen et al., 2006), thus providing a means for more accurate diagnosis and more accurate prognosis (Monti et al., 2010).Restingstate functional connectivity (FC) has been widely used to investigate pDOC and other neurological and psychiatric diseases (L.Wang et al., 2022).Currently common tools for assessing resting-state brain FC in pDOC patients include functional magnetic resonance imaging (fMRI) in relevant studies (Almdahl et al., 2021;Q. Chen et al., 2019;Hrybouski et al., 2021;Patil et al., 2021) and fNIRS, which facilitate the analysis of functional connections within and between brain networks without the need for subjects to perform a specific task.FMRI has excellent spatial resolution and can be better integrated with structural lesions (Ansado et al., 2021).Resting-state functional magnetic resonance imaging (rs-fMRI) has been recommended as a part of clinical multimodal evaluation of DOC and provides valuable information for brain network to detect possible subtle changes in brain activity (Norton et al., 2023;Snider & Edlow, 2020).Good agreement between fNIRS and fMRI results was found (Sasai et al., 2012).However, compared with fMRI, fNIRS is inexpensive, resistant to motion interference, resistant to electromagnetic interference, has high temporal and spatial resolution, and is easy to move (Cui et al., 2010).Therefore, fNIRS has great potential as a neuroimaging biomarker to enhance the diagnosis and disease monitoring of pDOC.
Research on FC has highlighted that the brain is intrinsically organized into distinct large-scale connectivity networks, which facilitate human brain function by their dynamic interplay (Fox et al., 2005).
Here, we focus on the major brain networks that have been identified in the past few years as being related to Consciousness.As we know, consciousness includes wakefulness and awareness (Naro et al., 2017), and awareness can be subdivided into two parts: environment (external) and self (internal) awareness (Demertzi et al., 2011).It has been identified that the frontoparietal network (FPN) is associated with external consciousness and that its metabolism is positively correlated with CRS-R scores (Leonardi et al., 2018;Mencarelli et al., 2020;Panda et al., 2022).The ventral attention network (VAN) has a right lateralization, is primarily responsible for non-spatial attention, and can participate in stimulus-driven top-down attentional selection (Deslauriers et al., 2017).The default mode network (DMN) exhibits internal activities, also referred to as the "task-negative network" (Andrews-Hanna, 2012;Andrews-Hanna et al., 2014), whereas the lateral frontoparietal areas related to the network of dorsal attention (DAN) mediate taskdriven stimuli (Mallas et al., 2021).In another case, according to the regulating function, the brain networks could be classified into higher order networks [the DMN and DAN] and sensory-related lower order networks including visual network (VIS) and sensorimotor network (SEN) (H.Li et al., 2023).Therefore, this study takes these six brain networks as the research object to explore the functional connection characteristics of pDOC cerebral cortex networks.

Participants
In this observational study, we recruited healthy volunteers and inpa-

Measurement tasks and execution methods
Basic information about all subjects was collected prior to assessment, including name, sex, age at baseline, symptoms, time of onset, site of stroke, current medical history, past medical history, family history of genetic disorders, previous treatments, imaging findings, and medication use.Three CRS-R (Seel et al., 2010) scores for pDOC patients and three MMSE scores for healthy adults within 1 week, with the highest score obtained in the three assessments as the basis for inclusion in pDOC and HC (Kalmar & Giacino, 2005), were obtained by assessment of a professional investigator not involved in the other assessments.

F I G U R E 1
Correspondence between functional near-infrared spectroscopy (fNIRS) acquisition headcaps and brain networks.DAN, dorsal attentional network; DMN, default mode network; FPN, frontoparietal network; SEN, sensorimotor network; VAN, ventral attentional network; VIS, visual network.

fNIRS measurement
We used a 63-channel desktop fNIRS device (Danyang Huitron) to capture the functional connectivity strength of cortical brain networks in the resting state of participants.Participants entered the assessment room and familiarized themselves with the environment for 5 min before putting on the fNIRS helmet and avoiding physical activity.The lighting in the assessment room was then switched off, and resting state data were collected from participants for 5 min (Kempny et al., 2016).
The SEN, DAN, VAN, FPN, DMN, and VIS of the cerebral cortex subdivision were selected according to the coverage of fNIRS channels and consciousness-related brain networks (Figure 1).

Preprocessing
Preprocessing of the fNIRS resting-state data was performed in the Preprocess module of the NirSpark software (H.Li et al., 2023;Zou et al., 2022).First, the motion artifacts were identified and removed using the spline interpolation method, and the signal standard deviation threshold was set to 6, and the peak threshold was set to 0.5.Then, the general noise including heartbeat and respiration was filtered with a band-pass filter of 0.01-0.2Hz.Finally, the path difference factor was set from −6 to 6, and the concentration changes of HbO and deoxyhemoglobin (HbR) in the resting state of the participant were calculated according to the modified Beer-Lamber law.

Statistical analysis
IBM SPSS Statistics for Windows (Version 27.0;IBM Corp.) was used for statistical analysis (Figure 2).Two-tailed p <.05 was considered a statistically significant difference.The Shapiro-Wilk test was used to assess whether the data obeyed a normal distribution.If the measurement data obeyed normal distribution, independent samples t-test was used for inter-group comparison.If they did not conform to a normal distribution, the Mann-Whitney U test was used.Factors with statistically significant differences were then analyzed by univariate binary logistic regression, and two-tailed p <.05 was considered a statistically significant difference.Finally, ROC curves were then plotted, and the area under the curve, sensitivity, and specificity were calculated, and the functional connectivity strength corresponding to the maximal Youden exponent was the optimal cut-off value.

Comparison of general data between the two groups
Ten patients with pDOC and 13 healthy adults were finally included in this study.There was no significant difference in age and sex between the two groups (p >.05), and the two groups were comparable (Table 1).

Characteristics and differences in functional connectivity of cortical brain networks between the healthy adult and pDOC groups based on HbO
Compared with the HC group, the strength of HbO-based functional connectivity in the pDOC group showed a decreasing trend in all six cortical brain networks (Figures 3), and the strength of HbO-based functional connectivity in five homologous brain networks (Figure 5a) and eight heterologous brain networks (Figure 5b) in the pDOC group was significantly lower (p <.05).

Characteristics and differences in functional connectivity of cortical brain networks between the healthy adult and pDOC groups based on HbR
Based on HbR to calculate the whole-brain functional connectivity of the two groups, the functional connectivity strength of the pDOC group in each brain network was still lower than that of the HC The flowchart for the data analysis.HbO, oxyhemoglobin; HbR, deoxyhemoglobin; HC, control group; pDOC, prolonged disorders of consciousness; ROC, receiver operating characteristic.group (Figures 4).Analyses of the strength of functional connectivity of homologous brain networks showed that three brain networks were significantly reduced in the pDOC group (Figure 5c).Analyses of the strength of functional connectivity of heterologous brain networks showed that eight brain networks were significantly reduced in the pDOC group (Figure 5d).

Univariate binary logistic regression analysis
Brain networks with statistically significant differences were analyzed by univariate binary logistic regression, B is the parameter estimate, SD is the standard error, OR is the odds ratio, Wals is the Wald value, and 95% CI is the 95% confidence interval, and two-tailed p < .05 was considered a statistically significant difference.
From Table 2, it can be seen that the connection strengths of HbO-

ROC curve analysis
To further explore the cut-off value of each brain functional connectivity network reflecting the participant's state of consciousness, ROC curves of their relationship with the participant's state of consciousness were plotted in this study.

HbO-based brain network
As can be seen from Figure 6

DISCUSSION
In this study, we explored the characteristics and differences in restingstate functional connectivity of cortical brain networks in pDOC and healthy adults by fNIRS and found that the strength of functional connectivity was lower in the pDOC group than in the healthy adult group, and that this trend was present in both homologous and heterologous brain networks.Relevant factors reflecting the participant's state of consciousness were also explored, providing a preclinical marker for predicting arousal in patients with pDOC.show that the sensitivity of the study using the HbO signals is higher than that using HbR signals, and the specificity of the study using HbR signals is higher than that using HbO signals.

Decreased strength of functional connectivity in pDOC cortical brain networks
Consciousness consists of two dimensions: the state of arousal and the content of consciousness.The cerebral cortical system, the thalamic system, and the brainstem ascending reticular activation system play an important role in maintaining human consciousness (Jang et al., 2019;Schiff, 2010).pDOC stems from direct interference with the neural systems that regulate arousal and consciousness, as well as indirect interference with the connections between these systems.The central loop model suggests that the direct deafferentation effects of central thalamic neurons and the cumulative effects of inhibition of striatal intermediate-type multispinal neurons (MSNs) activity combine to result in widespread reductions in synaptic activity throughout the brain and reduced brain metabolic rates, ultimately producing a range of unresponsive symptoms in patients with pDOC (Zheng et al., 2023).At the same time, recovery of consciousness is thought to be closely related to the restoration of connectivity within cortical thalamic neuronal activity (Edlow et al., 2021).Therefore, the present study used fNIRS to directly detect the strength of functional network connectivity in the brains of pDOC patients by bypassing behavioral responses, which could reveal the hidden consciousness and cognition in the brains of some patients earlier, thus providing a means for more accurate diagnosis and more accurate prognosis.In this study, we found that the strength of functional connectivity of homologous and heterologous brain networks in the pDOC group was lower than that in the healthy adult group.This suggests that pDOC diminishes the synergy between brain regions, and that by looking at the strength of functional connectivity, it may be possible to predict arousal in pDOC patients.

Brain networks reflecting subjects' states of consciousness
In this study, we captured six brain networks in the cerebral cortex, including SEN, DAN, VAN, DMN, FPN, and VIS. The SEN consists of the sensorimotor cortex, the supplementary motor area, and the secondary sensorimotor cortex, which are low-level functional brain regions that are mainly responsible for sensory and motor-related functions (Caspers et al., 2021).SEN is highly stable and is the first resting-state network to be discovered (Biswal et al., 1995).Sensory transport cortical integration is impaired in patients with pDOC, and reduced levels of neuronal metabolism are associated with pDOCrelated hypomotor function.In the present study, we found that based on the functional connectivity strength calculated from HbO and HbR, the connectivity strength between FPN, DAN, DMN, and SEN in the pDOC group was significantly reduced, as well as the connectivity strength between the homologous brain networks of the SEN, which may indicate that the reduction of sensory-motor functions as well as the impaired motor-sensory conduction are the important influencing factors of pDOC.Moreover, ROC curve analysis revealed that the cutoff value of SEN∼SEN brain functional connectivity strength, whether calculated on the basis of HbO or HbR, had the highest ability to predict the participant's state of consciousness.This shows that sensorimotor function is of great importance in the maintenance of the state of consciousness.
The DMN is an important resting-state brain network, mainly involved in episodic memory, self-reference, maintenance of arousal, and monitoring of the surrounding environment and is a high-level cognitive network region (Greicius et al., 2003).Damage to the DMN is closely related to a variety of neurological and psychiatric disorders such as Alzheimer's disease (Y.Chen et al., 2021), Parkinson's disease (Ruppert et al., 2021), epilepsy (Gonen et al., 2020), and autism (M.Wang et al., 2020).By comparing resting-state functional connectivity of different networks in MCS and UWS patients, Qin et al.(2015)found lower connectivity within the DMN in the UWS group, suggesting that the DMN predicts recovery of consciousness.In this study, the connection strength between VIS, SEN, and DMN in pDOC group were significantly reduced, and the connection strength between DMN homologous brain networks was also significantly reduced.When the functional connection strength of DMN∼DMN, SEN∼DMN, and VIS∼DMN calculated based on HbO were greater than or equal to 0.4442, 0.3286, and 0.2264, respectively, the probability of awakening was greater, which suggests that the impaired ability to perceive the external environment is one of the key factors affecting arousal in patients with pDOC.
Attention represents the initial aspect of cognitive processing and is a selective activity of consciousness.The DAN is bilaterally lateralized, provides top-down attentional orienting, and has an antagonistic relationship with the DMN (Mallas et al., 2021).In the present study, we found that the DAN was the network with the highest strength of functional connectivity among the six brain networks.And based on the functional connectivity strength calculated from HbO and HbR, the connectivity strength between SEN and DAN in the pDOC group was significantly reduced.The probability of participants awakening was greater when the resting-state functional connectivity strength of SEN∼DAN calculated based on HbO was greater than or equal to 0.4417 and when the resting-state functional connectivity strength of SEN∼DAN calculated based on HbR was greater than or equal to 0.1359.This implies that decreased connectivity between sensorimotor and attentional areas in patients with pDOC may affect their arousal.
The VAN has a right lateralization, is primarily responsible for nonspatial attention, and can be involved in stimulus-driven top-down attentional selection (Vossel et al., 2014).Deslauriers et al. (2017) found in fMRI that functional connectivity of brain regions in the posterior part of the VAN is elevated and that functional connectivity of brain regions in the anterior part of the VAN is reduced in older adults.
In this study, the connection strength between homologous and het-erologous brain networks of the VAN in the pDOC group did not differ from that of the healthy adult group based on the functional connection strength calculation of HbO; functional connectivity strength calculations based on HbR showed that the strength of heterologous brain network connections between SEN, FPN, DMN, and VAN were lower than in the healthy adult group.This result may be related to the inconsistent trend of functional connectivity changes in the anterior and posterior parts of the VAN.Therefore, this study does not recommend the functional connectivity strength of the VAN as a predictor of pDOC arousal.
FPN is mainly responsible for higher cognitive activities including processing and regulation of information processing of language, attention, vision, memory, and other related cognitive processes (Seeley et al., 2007).Long et al. (2016) showed that interactions between the dorsolateral prefrontal cortex in the FPN and the precuneus within the DMN may play a role in regulating consciousness, with greater connectivity between the two suggesting a greater likelihood of recovery of consciousness.In the present study, the connection strength between SEN, VIS, VAN, and FPN was significantly reduced in the pDOC group, as well as between FPN homologous brain networks.The connection strength between DMN and FPN was also reduced, but the difference was not significant, which may be related to the small sample size of this study.This may demonstrate that dysfunction of higher cognitive activities and its connection impairment with sensorimotor, visual, and memory is one of the main mechanisms of pDOC.In addition, the ROC curve analysis shows that the area under the curve of VIS∼FPN brain functional connection strength calculated based on HbO is 0.8846, which ranks third in predicting the conscious state of the subjects in all brain functional connection networks and can be used as the main preclinical biomarker for predicting the awakening state of pDOC patients.
The occipital region is the brain area where the VIS is located, with extensive fiber connections to the frontal and temporal lobes, and belongs to the lower brain functional areas (Yang et al., 2017).In this study, the connection strengths between FPN, DMN, and VIS were significantly reduced in the pDOC group.This suggests that visual dysfunction affects arousal in patients with pDOC.However, due to the difficulty of fNIRS in acquiring signals in the occipital region, we only lined up four channels, which did not fully cover the VIS-related region, and this result is subject to further refinement.

LIMITATION
First, the small sample size included in this study did not allow for a stratified analysis of the etiology and severity of the disease in pDOC patients.Second, fNIRS is an emerging noninvasive functional brain imaging technique in recent years, and its greatest advantages are its faster temporal resolution than fMRI techniques, its higher spatial resolution than EEG and SSEP techniques, and, more importantly, its portability and low artifact interference.Although its agreement with fMRI findings is high, there is a lack of comparison of its findings with those of EEG and SSEP, and electrophysiological indices such as EEG and SSEP should be tested simultaneously in this study to validate the results of the trial.In future studies, our group will try to expand the sample size and combine neuroimaging tools such as electrophysiology and fMRI to delve deeper into the brain networks associated with pDOC, and to further clarify the markers that predict pDOC arousal.Finally, this study covered six cortical brain networks with 63 channels, and the VIS was incompletely covered with only four channels.In future studies, we will choose the fNIRS headcap, which covers a wider range of brain regions, to explore pDOC-related mechanisms in depth, and to promote precise rehabilitation and treatment of pDOC.

CONCLUSION
In conclusion, pDOC is associated with extensive brain network changes affecting its short-and long-range connections, suggesting a possible mechanism for impaired consciousness in pDOC patients. Although HbO and HbR concentrations in the participant at each time point of the resting state measurement were extracted in the Network module of NirSpark software, and the Pearson correla-tion coefficients of HbO and HbR contents of each channel on the time series were analyzed.Then, FisherZ transformation was performed, and the transformed values were defined as the functional connection strength between channels.
, when the resting-state functional connectivity strengths of the HbO-based homologous brain functional connectivity networks SEN∼SEN, VIS∼VIS, DAN∼DAN, and DMN∼DMN were greater than or equal to 0.3557, 0.2540, 0.3902, F I G U R E 5 Statistical analysis of the differences in the strength of functional connectivity of each network in the cerebral cortex between the healthy adult group and the prolonged disorders of consciousness (pDOC) group.(a) Differential analysis of functional connectivity strength in homologous cortical brain networks based on oxyhemoglobin (HbO), (b) differential analysis of functional connectivity strength in heterologous cortical brain networks based on HbO, (c) differential analysis of functional connectivity strength in homologous cortical brain networks based on deoxyhemoglobin (HbR), and (d) differential analysis of functional connectivity strength in heterologous cortical brain networks based on HbR.HC, control group.

F I G U R E 6
Receiver operating characteristic (ROC) curve analysis of functional connectivity strength of homologous cortical brain networks based on oxyhemoglobin (HbO).(a) ROC of SEN∼SEN, (b) ROC of VIS∼VIS, (c) ROC of DAN∼DAN, and (d) ROC of DMN∼DMN.DAN, dorsal attentional network; DMN, default mode network; SEN, sensorimotor network; VAN, ventral attentional network; VIS, visual network.SEN∼VIS and VIS∼FPN predicting participant's state of consciousness more strongly.3.5.2HbR-based brain network ROC curve analysis of homologous and heterologous brain functional connection networks based on HbR is shown in Figure8aand b, respectively.When the resting-state functional connectivity strengths of SEN∼SEN and SEN∼DAN were greater than or equal to 0.2085 and 0.1359, respectively, the probability of participants' awakening was higher.The area under the curve for SEN∼SEN is the largest at 0.8769, which means that SEN∼SEN has a higher ability to predict the participant's state of consciousness.F I G U R E 7 Receiver operating characteristic (ROC) curve analysis of functional connectivity strength of heterogeneous cortical brain networks based on oxyhemoglobin (HbO).(a) ROC of SEN∼VIS, (b) ROC of SEN∼FPN, (c) ROC of SEN∼DAN, (d) ROC of SEN∼DMN, (e) ROC of VIS∼FPN, (f) ROC of VIS∼DAN, and (g) ROC of VIS∼DMN.DAN, dorsal attentional network; DMN, default mode network; FPN, frontoparietal network; SEN, sensorimotor network; VAN, ventral attentional network; VIS, visual network.F I G U R E 8 Receiver operating characteristic (ROC) curve analysis of functional connectivity strength of cortical brain networks based on HbR.(a) ROC of SEN∼SEN and (b) ROC of SEN∼DAN.SEN, sensorimotor network; DAN, dorsal attentional network.
fNIRS can detect changes in the concentration of HbO and HbR in the cerebral cortex of subjects in real time.Theoretically, HbO and HbR are highly correlated.However, in practical use, there is noise interference in real HbO and HbR data, and recording HbO and HbR at the same time can significantly increase the correct rate of braincomputer interface (Cui et al., 2010).So, we used both HbO and HbR signals from patients.The results were as follows: first, compared to the healthy adult group, the whole-brain mean functional connectivity strength either based on HbO or HbR was significantly decreased in the pDOC group.Second, the results of intergroup comparison of HbO-based homologous brain networks showed a significant decrease in the functional connectivity strength of five brain networks in the pDOC group, which included the results of intergroup comparison of HbR-based homologous brain networks, and the results of intergroup comparison of both HbO-and HbR-based heterologous brain networks showed a significant decrease in the functional connectivity strength of eight brain networks, of which five heterologous brain networks were overlapped.Finally, univariate binary logistic regression analysis and ROC curve analysis showed that 11 of the HbO-based brain networks could reflect the subjects' conscious state, while only two of the HbR-based brain networks could reflect the subjects' conscious state and were included in the 11 HbO-based brain networks.The above results Univariate binary logistic regression analysis of functional connectivity strength in homologous brain networks based on oxyhemoglobin (HbO).Univariate binary logistic regression analysis of functional connectivity strength in homologous brain networks based on deoxyhemoglobin (HbR).Univariate binary logistic regression analysis of functional connectivity strength in heterologous brain networks based on deoxyhemoglobin (HbR).
Abbreviations: B, parameter estimate; CI, confidence interval; DAN, dorsal attentional network; DMN, default mode network; FPN, frontoparietal network; OR, odds ratio; SEN, sensorimotor network; VAN, ventral attentional network; VIS, visual network.*p<0.05and**p < 0.01.TA B L E 3Univariate binary logistic regression analysis of functional connectivity strength in heterologous brain networks based on oxyhemoglobin (HbO).and0.4442,respectively, the probability of participants awakening was higher.The areas under the curves for SEN∼SEN and VIS∼VIS were the largest, 0.9308 and 0.9231, respectively, which means that SEN∼SEN and VIS∼VIS were more capable of predicting the participants' state of consciousness.Figure7shows that when the resting functional connectivity strengths of HbO-based heterogeneous brain functional connection networks SEN∼VIS, SEN∼FPN, SEN∼DAN, SEN∼DMN, VIS∼FPN, VIS∼DAN, and VIS∼DMN were greater than or equal to 0.1626, 0.2589, 0.4417, 0.3286, 0.1821, 0.2458, and 0.2264, respectively, the probability of participants awakening was greater, with TA B L E 4 Abbreviations: B, parameter estimate; CI, confidence interval; DAN, dorsal attentional network; DMN, default mode network; FPN, frontoparietal network; OR, odds ratio; SEN, sensorimotor network; VAN, ventral attentional network; VIS, visual network.*p < 0.05 and **p < 0.01.
additional experiments are necessary, cut-off values of brain networks reflecting participants' state of consciousness could serve as potential preclinical markers for predicting arousal in pDOC patients.